Due to the COVID-19 pandemic, many Computer Vision-based works have been recently published to help monitor human activities analyzing images, especially on the specific task of evaluating the social distance between people. For example, the Inter-Homines system, presented in [1]}, evaluates in real-time the contagion risk in a monitored area by analyzing video streams. The system includes occlusion correction, homography transformation, and people anonymization. People are located in the space exploiting the CenterNet [2]} object detector, and interpersonal distances are then calculated. Results are evaluated on the jta dataset [3]} (i.e., in a virtual world). In [4]}, the YOLO9000 detector [5]} has been exploited to detect people, and centroids of the found bounding boxes are computed to evaluate the distance between them. Similarly, in [6]}, a platform for social distance tracking in top perspective video frames based on YOLOv3 [7]} was presented. Here too, centroids of the bounding boxes are used to estimate distances. A subset of the authors of [6]} also presented in [9]} a social distance framework based on the Faster-RCNN detector [10]}. Instead, YOLOv3 [7]} to detect humans and Deepsort [12]} to track people are exploited in [13]}. Experiments are conducted on the Oxford town center surveillance footages [14]}.
Monitoring of workers to detect social distancing violation that uses Mobilenet-V2 [15]} to detect people is introduced in [16]}.
